1. Field of the Disclosure
The present disclosure relates to parallel distributed computer systems for simulating neuronal networks that perform neural computations, such as visual perception, motor control, and/or other neural computations.
2. Description of Related Art
Most neuronal models and systems consist of networks of simple units, called neurons, which interact with each other and with the external world via connections called synapses. The information processing in such neuronal systems may be carried out in parallel.
Some existing parallel hardware architectures and corresponding languages that may be optimized for parallel execution and simulation of neuronal models may utilize event-driven network updates. When implementing learning in neural networks, it may be desirable to be able to utilize events that may selectively modify some aspects of the network (e.g., change synaptic weights), while keeping the other aspects of the network unaffected (e.g., modify these weights without generating neuron output).
However, existing implementations, utilize events that combine connection weight adaptation and post-synaptic response generation for a neuron of the network.
Accordingly, there is a salient need for additional mechanisms that may decouple network-wide connection adaptation from post-synaptic response generation in a network thereby allowing for more flexible implementation of learning algorithms in spiking neuron networks.